Reduced rank modeling for functional regression with functional responses

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

  • Hongmei Lin
  • Xuejun Jiang
  • Heng Lian
  • Weiping Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)205-217
Journal / PublicationJournal of Multivariate Analysis
Volume169
StatePublished - Jan 2019

Abstract

This article considers regression problems where both the predictor and the response are functional in nature. Driven by the desire to build a parsimonious model, we consider functional reduced rank regression in the framework of reproducing kernel Hilbert spaces, which can be formulated in the form of linear factor regression with estimated multivariate factors, and achieves dimension reduction in both the predictor and the response spaces. The convergence rate of the estimator is derived. Simulations and real datasets are used to demonstrate the competitive performance of the proposed method.

Research Area(s)

  • Dimension reduction, Functional data, Functional response, Reproducing kernel Hilbert space

Citation Format(s)

Reduced rank modeling for functional regression with functional responses. / Lin, Hongmei; Jiang, Xuejun; Lian, Heng; Zhang, Weiping.

In: Journal of Multivariate Analysis, Vol. 169, 01.2019, p. 205-217.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review